Copyright¶
Copyright 2021 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
In [1]:
#@title Collapsed copyright
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Demo for open-vocabulary ViLD object detector¶
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation, Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, Yin Cui.
The ViLD model in this colab has a backbone of ResNet-152 and the distillation weight is 0.1. It achieves 16.8 APr and 26.4 overall AP on LVIS.
For faster inference speed, please use a GPU runtime. You can use your local GPU runtime or change the runtime type to "GPU" from Runtime -> Change Runtime Type in the menu.
Preparation¶
In [1]:
#@title Install dependencies
! pip install git+https://github.com/openai/CLIP.git
In [3]:
! pip install gsutil
In [4]:
#@title Download files
!gsutil cp -r gs://cloud-tpu-checkpoints/detection/projects/vild/colab/image_path_v2 ./
#@ About Pb File : https://zhuanlan.zhihu.com/p/32887066
#// image_path_v2
#// ├── saved_model.pb
#// └── variables
#// ├── variables.data-00000-of-00001
#// └── variables.index
!gsutil cp -r gs://cloud-tpu-checkpoints/detection/projects/vild/colab/examples ./
#@ examples have jpg img
#@ Download and unzip the LVIS v1.0 validation sets to DATA_DIR.
In [11]:
! pip install easyDict
! pip install scipy
! pip install pyyaml
In [12]:
#@title Import libraries
from easydict import EasyDict
import numpy as np
import torch
import clip
from tqdm import tqdm
from matplotlib import pyplot as plt
from matplotlib import patches
import collections
import json
import numpy as np
import os
import os.path as osp
from PIL import Image
from pprint import pprint
from scipy.special import softmax
import yaml
import tensorflow.compat.v1 as tf
import cv2
In [13]:
#@title Define hyperparameters
FLAGS = {
'prompt_engineering': True,
'this_is': True,
'temperature': 100.0,
'use_softmax': False,
}
FLAGS = EasyDict(FLAGS)
# Global matplotlib settings
SMALL_SIZE = 16#10
MEDIUM_SIZE = 18#12
BIGGER_SIZE = 20#14
plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
# Parameters for drawing figure.
display_input_size = (10, 10)
overall_fig_size = (18, 24)
line_thickness = 2
fig_size_w = 35
# fig_size_h = min(max(5, int(len(category_names) / 2.5) ), 10)
mask_color = 'red'
alpha = 0.5
Build text embeddings¶
We use the CLIP model from OpenAI: https://github.com/openai/CLIP.
In [14]:
def article(name):
return 'an' if name[0] in 'aeiou' else 'a'
def processed_name(name, rm_dot=False):
# _ for lvis
# / for obj365
res = name.replace('_', ' ').replace('/', ' or ').lower()
if rm_dot:
res = res.rstrip('.')
return res
single_template = [
'a photo of {article} {}.'
]
multiple_templates = [
'There is {article} {} in the scene.',
'There is the {} in the scene.',
'a photo of {article} {} in the scene.',
'a photo of the {} in the scene.',
'a photo of one {} in the scene.',
'itap of {article} {}.',
'itap of my {}.', # itap: I took a picture of
'itap of the {}.',
'a photo of {article} {}.',
'a photo of my {}.',
'a photo of the {}.',
'a photo of one {}.',
'a photo of many {}.',
'a good photo of {article} {}.',
'a good photo of the {}.',
'a bad photo of {article} {}.',
'a bad photo of the {}.',
'a photo of a nice {}.',
'a photo of the nice {}.',
'a photo of a cool {}.',
'a photo of the cool {}.',
'a photo of a weird {}.',
'a photo of the weird {}.',
'a photo of a small {}.',
'a photo of the small {}.',
'a photo of a large {}.',
'a photo of the large {}.',
'a photo of a clean {}.',
'a photo of the clean {}.',
'a photo of a dirty {}.',
'a photo of the dirty {}.',
'a bright photo of {article} {}.',
'a bright photo of the {}.',
'a dark photo of {article} {}.',
'a dark photo of the {}.',
'a photo of a hard to see {}.',
'a photo of the hard to see {}.',
'a low resolution photo of {article} {}.',
'a low resolution photo of the {}.',
'a cropped photo of {article} {}.',
'a cropped photo of the {}.',
'a close-up photo of {article} {}.',
'a close-up photo of the {}.',
'a jpeg corrupted photo of {article} {}.',
'a jpeg corrupted photo of the {}.',
'a blurry photo of {article} {}.',
'a blurry photo of the {}.',
'a pixelated photo of {article} {}.',
'a pixelated photo of the {}.',
'a black and white photo of the {}.',
'a black and white photo of {article} {}.',
'a plastic {}.',
'the plastic {}.',
'a toy {}.',
'the toy {}.',
'a plushie {}.',
'the plushie {}.',
'a cartoon {}.',
'the cartoon {}.',
'an embroidered {}.',
'the embroidered {}.',
'a painting of the {}.',
'a painting of a {}.',
]
In [15]:
clip.available_models()
model, preprocess = clip.load("ViT-B/32")
In [16]:
def build_text_embedding(categories):
if FLAGS.prompt_engineering:
templates = multiple_templates
else:
templates = single_template
run_on_gpu = torch.cuda.is_available()
with torch.no_grad():
all_text_embeddings = []
print('Building text embeddings...')
for category in tqdm(categories):
texts = [
template.format(processed_name(category['name'], rm_dot=True),
article=article(category['name']))
for template in templates]
if FLAGS.this_is:
texts = [
'This is ' + text if text.startswith('a') or text.startswith('the') else text
for text in texts
]
texts = clip.tokenize(texts) #tokenize
if run_on_gpu:
texts = texts.cuda()
text_embeddings = model.encode_text(texts) #embed with text encoder
text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)
text_embedding = text_embeddings.mean(dim=0)
text_embedding /= text_embedding.norm()
all_text_embeddings.append(text_embedding)
all_text_embeddings = torch.stack(all_text_embeddings, dim=1)
if run_on_gpu:
all_text_embeddings = all_text_embeddings.cuda()
return all_text_embeddings.cpu().numpy().T
Load ViLD model¶
In [17]:
session = tf.Session(graph=tf.Graph())
In [18]:
saved_model_dir = './image_path_v2' #@param {type:"string"}
_ = tf.saved_model.loader.load(session, ['serve'], saved_model_dir)
Helper functioins¶
In [19]:
numbered_categories = [{'name': str(idx), 'id': idx,} for idx in range(50)]
numbered_category_indices = {cat['id']: cat for cat in numbered_categories}
In [21]:
#@title NMS
def nms(dets, scores, thresh, max_dets=1000):
"""Non-maximum suppression.
Args:
dets: [N, 4]
scores: [N,]
thresh: iou threshold. Float
max_dets: int.
"""
y1 = dets[:, 0]
x1 = dets[:, 1]
y2 = dets[:, 2]
x2 = dets[:, 3]
areas = (x2 - x1) * (y2 - y1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0 and len(keep) < max_dets:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
intersection = w * h
overlap = intersection / (areas[i] + areas[order[1:]] - intersection + 1e-12)
inds = np.where(overlap <= thresh)[0]
order = order[inds + 1]
return keep
In [22]:
#@title Visualization
import PIL.ImageColor as ImageColor
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
STANDARD_COLORS = [
'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Cyan',
'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FloralWhite',
'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'Ivory', 'Khaki',
'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange',
'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
'WhiteSmoke', 'Yellow', 'YellowGreen'
]
def draw_bounding_box_on_image(image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
"""Adds a bounding box to an image.
Bounding box coordinates can be specified in either absolute (pixel) or
normalized coordinates by setting the use_normalized_coordinates argument.
Each string in display_str_list is displayed on a separate line above the
bounding box in black text on a rectangle filled with the input 'color'.
If the top of the bounding box extends to the edge of the image, the strings
are displayed below the bounding box.
Args:
image: a PIL.Image object.
ymin: ymin of bounding box.
xmin: xmin of bounding box.
ymax: ymax of bounding box.
xmax: xmax of bounding box.
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list: list of strings to display in box
(each to be shown on its own line).
use_normalized_coordinates: If True (default), treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute.
"""
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
if use_normalized_coordinates:
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
else:
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
draw.line([(left, top), (left, bottom), (right, bottom),
(right, top), (left, top)], width=thickness, fill=color)
try:
font = ImageFont.truetype('arial.ttf', 24)
except IOError:
font = ImageFont.load_default()
# If the total height of the display strings added to the top of the bounding
# box exceeds the top of the image, stack the strings below the bounding box
# instead of above.
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
# Each display_str has a top and bottom margin of 0.05x.
total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = bottom + total_display_str_height
# Reverse list and print from bottom to top.
for display_str in display_str_list[::-1]:
text_left = min(5, left)
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle(
[(left, text_bottom - text_height - 2 * margin), (left + text_width,
text_bottom)],
fill=color)
draw.text(
(left + margin, text_bottom - text_height - margin),
display_str,
fill='black',
font=font)
text_bottom -= text_height - 2 * margin
def draw_bounding_box_on_image_array(image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
"""Adds a bounding box to an image (numpy array).
Bounding box coordinates can be specified in either absolute (pixel) or
normalized coordinates by setting the use_normalized_coordinates argument.
Args:
image: a numpy array with shape [height, width, 3].
ymin: ymin of bounding box.
xmin: xmin of bounding box.
ymax: ymax of bounding box.
xmax: xmax of bounding box.
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list: list of strings to display in box
(each to be shown on its own line).
use_normalized_coordinates: If True (default), treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute.
"""
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
thickness, display_str_list,
use_normalized_coordinates)
np.copyto(image, np.array(image_pil))
def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
"""Draws mask on an image.
Args:
image: uint8 numpy array with shape (img_height, img_height, 3)
mask: a uint8 numpy array of shape (img_height, img_height) with
values between either 0 or 1.
color: color to draw the keypoints with. Default is red.
alpha: transparency value between 0 and 1. (default: 0.4)
Raises:
ValueError: On incorrect data type for image or masks.
"""
if image.dtype != np.uint8:
raise ValueError('`image` not of type np.uint8')
if mask.dtype != np.uint8:
raise ValueError('`mask` not of type np.uint8')
if np.any(np.logical_and(mask != 1, mask != 0)):
raise ValueError('`mask` elements should be in [0, 1]')
if image.shape[:2] != mask.shape:
raise ValueError('The image has spatial dimensions %s but the mask has '
'dimensions %s' % (image.shape[:2], mask.shape))
rgb = ImageColor.getrgb(color)
pil_image = Image.fromarray(image)
solid_color = np.expand_dims(
np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
np.copyto(image, np.array(pil_image.convert('RGB')))
def visualize_boxes_and_labels_on_image_array(
image,
boxes,
classes,
scores,
category_index,
instance_masks=None,
instance_boundaries=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4,
groundtruth_box_visualization_color='black',
skip_scores=False,
skip_labels=False,
mask_alpha=0.4,
plot_color=None,
):
"""Overlay labeled boxes on an image with formatted scores and label names.
This function groups boxes that correspond to the same location
and creates a display string for each detection and overlays these
on the image. Note that this function modifies the image in place, and returns
that same image.
Args:
image: uint8 numpy array with shape (img_height, img_width, 3)
boxes: a numpy array of shape [N, 4]
classes: a numpy array of shape [N]. Note that class indices are 1-based,
and match the keys in the label map.
scores: a numpy array of shape [N] or None. If scores=None, then
this function assumes that the boxes to be plotted are groundtruth
boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category indices.
instance_masks: a numpy array of shape [N, image_height, image_width] with
values ranging between 0 and 1, can be None.
instance_boundaries: a numpy array of shape [N, image_height, image_width]
with values ranging between 0 and 1, can be None.
use_normalized_coordinates: whether boxes is to be interpreted as
normalized coordinates or not.
max_boxes_to_draw: maximum number of boxes to visualize. If None, draw
all boxes.
min_score_thresh: minimum score threshold for a box to be visualized
agnostic_mode: boolean (default: False) controlling whether to evaluate in
class-agnostic mode or not. This mode will display scores but ignore
classes.
line_thickness: integer (default: 4) controlling line width of the boxes.
groundtruth_box_visualization_color: box color for visualizing groundtruth
boxes
skip_scores: whether to skip score when drawing a single detection
skip_labels: whether to skip label when drawing a single detection
Returns:
uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
"""
# Create a display string (and color) for every box location, group any boxes
# that correspond to the same location.
box_to_display_str_map = collections.defaultdict(list)
box_to_color_map = collections.defaultdict(str)
box_to_instance_masks_map = {}
box_to_score_map = {}
box_to_instance_boundaries_map = {}
if not max_boxes_to_draw:
max_boxes_to_draw = boxes.shape[0]
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
if instance_masks is not None:
box_to_instance_masks_map[box] = instance_masks[i]
if instance_boundaries is not None:
box_to_instance_boundaries_map[box] = instance_boundaries[i]
if scores is None:
box_to_color_map[box] = groundtruth_box_visualization_color
else:
display_str = ''
if not skip_labels:
if not agnostic_mode:
if classes[i] in list(category_index.keys()):
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = str(class_name)
if not skip_scores:
if not display_str:
display_str = '{}%'.format(int(100*scores[i]))
else:
float_score = ("%.2f" % scores[i]).lstrip('0')
display_str = '{}: {}'.format(display_str, float_score)
box_to_score_map[box] = int(100*scores[i])
box_to_display_str_map[box].append(display_str)
if plot_color is not None:
box_to_color_map[box] = plot_color
elif agnostic_mode:
box_to_color_map[box] = 'DarkOrange'
else:
box_to_color_map[box] = STANDARD_COLORS[
classes[i] % len(STANDARD_COLORS)]
# Handle the case when box_to_score_map is empty.
if box_to_score_map:
box_color_iter = sorted(
box_to_color_map.items(), key=lambda kv: box_to_score_map[kv[0]])
else:
box_color_iter = box_to_color_map.items()
# Draw all boxes onto image.
for box, color in box_color_iter:
ymin, xmin, ymax, xmax = box
if instance_masks is not None:
draw_mask_on_image_array(
image,
box_to_instance_masks_map[box],
color=color,
alpha=mask_alpha
)
if instance_boundaries is not None:
draw_mask_on_image_array(
image,
box_to_instance_boundaries_map[box],
color='red',
alpha=1.0
)
draw_bounding_box_on_image_array(
image,
ymin,
xmin,
ymax,
xmax,
color=color,
thickness=line_thickness,
display_str_list=box_to_display_str_map[box],
use_normalized_coordinates=use_normalized_coordinates)
return image
def paste_instance_masks(masks,
detected_boxes,
image_height,
image_width):
"""Paste instance masks to generate the image segmentation results.
Args:
masks: a numpy array of shape [N, mask_height, mask_width] representing the
instance masks w.r.t. the `detected_boxes`.
detected_boxes: a numpy array of shape [N, 4] representing the reference
bounding boxes.
image_height: an integer representing the height of the image.
image_width: an integer representing the width of the image.
Returns:
segms: a numpy array of shape [N, image_height, image_width] representing
the instance masks *pasted* on the image canvas.
"""
def expand_boxes(boxes, scale):
"""Expands an array of boxes by a given scale."""
# Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/boxes.py#L227 # pylint: disable=line-too-long
# The `boxes` in the reference implementation is in [x1, y1, x2, y2] form,
# whereas `boxes` here is in [x1, y1, w, h] form
w_half = boxes[:, 2] * .5
h_half = boxes[:, 3] * .5
x_c = boxes[:, 0] + w_half
y_c = boxes[:, 1] + h_half
w_half *= scale
h_half *= scale
boxes_exp = np.zeros(boxes.shape)
boxes_exp[:, 0] = x_c - w_half
boxes_exp[:, 2] = x_c + w_half
boxes_exp[:, 1] = y_c - h_half
boxes_exp[:, 3] = y_c + h_half
return boxes_exp
# Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/core/test.py#L812 # pylint: disable=line-too-long
# To work around an issue with cv2.resize (it seems to automatically pad
# with repeated border values), we manually zero-pad the masks by 1 pixel
# prior to resizing back to the original image resolution. This prevents
# "top hat" artifacts. We therefore need to expand the reference boxes by an
# appropriate factor.
_, mask_height, mask_width = masks.shape
scale = max((mask_width + 2.0) / mask_width,
(mask_height + 2.0) / mask_height)
ref_boxes = expand_boxes(detected_boxes, scale)
ref_boxes = ref_boxes.astype(np.int32)
padded_mask = np.zeros((mask_height + 2, mask_width + 2), dtype=np.float32)
segms = []
for mask_ind, mask in enumerate(masks):
im_mask = np.zeros((image_height, image_width), dtype=np.uint8)
# Process mask inside bounding boxes.
padded_mask[1:-1, 1:-1] = mask[:, :]
ref_box = ref_boxes[mask_ind, :]
w = ref_box[2] - ref_box[0] + 1
h = ref_box[3] - ref_box[1] + 1
w = np.maximum(w, 1)
h = np.maximum(h, 1)
mask = cv2.resize(padded_mask, (w, h))
mask = np.array(mask > 0.5, dtype=np.uint8)
x_0 = min(max(ref_box[0], 0), image_width)
x_1 = min(max(ref_box[2] + 1, 0), image_width)
y_0 = min(max(ref_box[1], 0), image_height)
y_1 = min(max(ref_box[3] + 1, 0), image_height)
im_mask[y_0:y_1, x_0:x_1] = mask[
(y_0 - ref_box[1]):(y_1 - ref_box[1]),
(x_0 - ref_box[0]):(x_1 - ref_box[0])
]
segms.append(im_mask)
segms = np.array(segms)
assert masks.shape[0] == segms.shape[0]
return segms
In [23]:
#@title Plot instance masks
def plot_mask(color, alpha, original_image, mask):
rgb = ImageColor.getrgb(color)
pil_image = Image.fromarray(original_image)
solid_color = np.expand_dims(
np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
img_w_mask = np.array(pil_image.convert('RGB'))
return img_w_mask
Main functions¶
In [24]:
%matplotlib inline
def display_image(path_or_array, size=(10, 10)):
if isinstance(path_or_array, str):
image = np.asarray(Image.open(open(image_path, 'rb')).convert("RGB"))
else:
image = path_or_array
plt.figure(figsize=size)
plt.imshow(image)
plt.axis('off')
plt.show()
In [25]:
def main(image_path, category_name_string, params):
#################################################################
# Preprocessing categories and get params
category_names = [x.strip() for x in category_name_string.split(';')]
category_names = ['background'] + category_names
categories = [{'name': item, 'id': idx+1,} for idx, item in enumerate(category_names)]
category_indices = {cat['id']: cat for cat in categories}
max_boxes_to_draw, nms_threshold, min_rpn_score_thresh, min_box_area = params
fig_size_h = min(max(5, int(len(category_names) / 2.5) ), 10)
#################################################################
# Obtain results and read image
roi_boxes, roi_scores, detection_boxes, scores_unused, box_outputs, detection_masks, visual_features, image_info = session.run(
['RoiBoxes:0', 'RoiScores:0', '2ndStageBoxes:0', '2ndStageScoresUnused:0', 'BoxOutputs:0', 'MaskOutputs:0', 'VisualFeatOutputs:0', 'ImageInfo:0'],
feed_dict={'Placeholder:0': [image_path,]})
roi_boxes = np.squeeze(roi_boxes, axis=0) # squeeze
# no need to clip the boxes, already done
roi_scores = np.squeeze(roi_scores, axis=0)
detection_boxes = np.squeeze(detection_boxes, axis=(0, 2))
scores_unused = np.squeeze(scores_unused, axis=0)
box_outputs = np.squeeze(box_outputs, axis=0)
detection_masks = np.squeeze(detection_masks, axis=0)
visual_features = np.squeeze(visual_features, axis=0)
image_info = np.squeeze(image_info, axis=0) # obtain image info
image_scale = np.tile(image_info[2:3, :], (1, 2))
image_height = int(image_info[0, 0])
image_width = int(image_info[0, 1])
rescaled_detection_boxes = detection_boxes / image_scale # rescale
# Read image
image = np.asarray(Image.open(open(image_path, 'rb')).convert("RGB"))
assert image_height == image.shape[0]
assert image_width == image.shape[1]
#################################################################
# Filter boxes
# Apply non-maximum suppression to detected boxes with nms threshold.
nmsed_indices = nms(
detection_boxes,
roi_scores,
thresh=nms_threshold
)
# Compute RPN box size.
box_sizes = (rescaled_detection_boxes[:, 2] - rescaled_detection_boxes[:, 0]) * (rescaled_detection_boxes[:, 3] - rescaled_detection_boxes[:, 1])
# Filter out invalid rois (nmsed rois)
valid_indices = np.where(
np.logical_and(
np.isin(np.arange(len(roi_scores), dtype=np.int), nmsed_indices),
np.logical_and(
np.logical_not(np.all(roi_boxes == 0., axis=-1)),
np.logical_and(
roi_scores >= min_rpn_score_thresh,
box_sizes > min_box_area
)
)
)
)[0]
print('number of valid indices', len(valid_indices))
detection_roi_scores = roi_scores[valid_indices][:max_boxes_to_draw, ...]
detection_boxes = detection_boxes[valid_indices][:max_boxes_to_draw, ...]
detection_masks = detection_masks[valid_indices][:max_boxes_to_draw, ...]
detection_visual_feat = visual_features[valid_indices][:max_boxes_to_draw, ...]
rescaled_detection_boxes = rescaled_detection_boxes[valid_indices][:max_boxes_to_draw, ...]
#################################################################
# Compute text embeddings and detection scores, and rank results
text_features = build_text_embedding(categories)
raw_scores = detection_visual_feat.dot(text_features.T)
if FLAGS.use_softmax:
scores_all = softmax(FLAGS.temperature * raw_scores, axis=-1)
else:
scores_all = raw_scores
indices = np.argsort(-np.max(scores_all, axis=1)) # Results are ranked by scores
indices_fg = np.array([i for i in indices if np.argmax(scores_all[i]) != 0])
#################################################################
# Plot detected boxes on the input image.
ymin, xmin, ymax, xmax = np.split(rescaled_detection_boxes, 4, axis=-1)
processed_boxes = np.concatenate([xmin, ymin, xmax - xmin, ymax - ymin], axis=-1)
segmentations = paste_instance_masks(detection_masks, processed_boxes, image_height, image_width)
if len(indices_fg) == 0:
display_image(np.array(image), size=overall_fig_size)
print('ViLD does not detect anything belong to the given category')
else:
image_with_detections = visualize_boxes_and_labels_on_image_array(
np.array(image),
rescaled_detection_boxes[indices_fg],
valid_indices[:max_boxes_to_draw][indices_fg],
detection_roi_scores[indices_fg],
numbered_category_indices,
instance_masks=segmentations[indices_fg],
use_normalized_coordinates=False,
max_boxes_to_draw=max_boxes_to_draw,
min_score_thresh=min_rpn_score_thresh,
skip_scores=False,
skip_labels=True)
plt.figure(figsize=overall_fig_size)
plt.imshow(image_with_detections)
plt.axis('off')
plt.title('Detected objects and RPN scores')
plt.show()
#################################################################
# Plot
cnt = 0
raw_image = np.array(image)
n_boxes = rescaled_detection_boxes.shape[0]
for anno_idx in indices[0:int(n_boxes)]:
rpn_score = detection_roi_scores[anno_idx]
bbox = rescaled_detection_boxes[anno_idx]
scores = scores_all[anno_idx]
if np.argmax(scores) == 0:
continue
y1, x1, y2, x2 = int(np.floor(bbox[0])), int(np.floor(bbox[1])), int(np.ceil(bbox[2])), int(np.ceil(bbox[3]))
img_w_mask = plot_mask(mask_color, alpha, raw_image, segmentations[anno_idx])
crop_w_mask = img_w_mask[y1:y2, x1:x2, :]
fig, axs = plt.subplots(1, 4, figsize=(fig_size_w, fig_size_h), gridspec_kw={'width_ratios': [3, 1, 1, 2]}, constrained_layout=True)
# Draw bounding box.
rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=line_thickness, edgecolor='r', facecolor='none')
axs[0].add_patch(rect)
axs[0].set_xticks([])
axs[0].set_yticks([])
axs[0].set_title(f'bbox: {y1, x1, y2, x2} area: {(y2 - y1) * (x2 - x1)} rpn score: {rpn_score:.4f}')
axs[0].imshow(raw_image)
# Draw image in a cropped region.
crop = np.copy(raw_image[y1:y2, x1:x2, :])
axs[1].set_xticks([])
axs[1].set_yticks([])
axs[1].set_title(f'predicted: {category_names[np.argmax(scores)]}')
axs[1].imshow(crop)
# Draw segmentation inside a cropped region.
axs[2].set_xticks([])
axs[2].set_yticks([])
axs[2].set_title('mask')
axs[2].imshow(crop_w_mask)
# Draw category scores.
fontsize = max(min(fig_size_h / float(len(category_names)) * 45, 20), 8)
for cat_idx in range(len(category_names)):
axs[3].barh(cat_idx, scores[cat_idx],
color='orange' if scores[cat_idx] == max(scores) else 'blue')
axs[3].invert_yaxis()
axs[3].set_axisbelow(True)
axs[3].set_xlim(0, 1)
plt.xlabel("confidence score")
axs[3].set_yticks(range(len(category_names)))
axs[3].set_yticklabels(category_names, fontdict={
'fontsize': fontsize})
cnt += 1
# fig.tight_layout()
print('Detection counts:', cnt)
Start playing¶
Five women and umbrellas¶
In [26]:
image_path = './examples/five_women_and_umbrellas.jpg' #@param {type:"string"}
display_image(image_path, size=display_input_size)
In [27]:
category_name_string = ';'.join(['flipflop', 'street sign', 'bracelet',
'necklace', 'shorts', 'floral camisole', 'orange shirt',
'purple dress', 'yellow tee', 'green umbrella', 'pink striped umbrella',
'transparent umbrella', 'plain pink umbrella', 'blue patterned umbrella',
'koala', 'electric box','car', 'pole'])
max_boxes_to_draw = 25 #@param {type:"integer"}
nms_threshold = 0.6 #@param {type:"slider", min:0, max:0.9, step:0.05}
min_rpn_score_thresh = 0.9 #@param {type:"slider", min:0, max:1, step:0.01}
min_box_area = 220 #@param {type:"slider", min:0, max:10000, step:1.0}
params = max_boxes_to_draw, nms_threshold, min_rpn_score_thresh, min_box_area
main(image_path, category_name_string, params)
#@markdown Orange bar means the prediction with maximum score over text inputs.
